A framework for analyzing converging feedforward and cortical-bulbar feedback dynamics in target detection from complex odor scenes

Cold Spring Harbor Laboratory, Cold Spg Hbr NY

Investigators

Abstract

This project makes use of recent advances in optical imaging and optogenetic strategies to monitor the brain at work. Specifically, the project is focused on understanding the interplay between ascending and descending (feedback) activity patterns in the olfactory system of behaving mice. Here, the investigator does not simply focus on the olfactory sensory module that integrates and transmits information from the nose to the brain but determines how higher brain areas, namely, the olfactory cortex, interact in the recurrent processing loop. This strategy enables the investigator to evaluate how sensory inputs are shaped by internal brain states via feedback. Furthermore, the investigator works at the interface of two approaches by combining cutting-edge experimental approaches--optical imaging and optogenetic strategies-- with novel computational models that give rise to non-mutually exclusive testable predictions. The investigator determines whether these feedforward-feedback loops contribute to attention states, extraction of odor identity, or broadcasting of predictions and error signals related to the incoming odorants. Experimental techniques are complemented, through an international collaboration, with state-of-the-art data analysis that characterizes neuronal population dynamics along high-dimensional trajectories and measures occurrence of activity patterns, characteristic timescales, patterns interaction, and coordination as a function of behavior. Additionally, the project provides opportunities for students and postdoctoral trainees from the USA and Romania to expand their experimental and computational skills through their participation in the international collaboration. A central goal of systems neuroscience is to describe behaviors in terms of the neuronal circuits that control them. This constitutes a steep challenge in the mammalian brain, because behaviors are thought to rely on widely distributed feedforward, as well as top-down feedback neural representations, which are technically difficult to monitor at large scales and manipulate at cellular resolution. The project builds on recent experimental results from the lead investigator and novel algorithms for odor identification developed by the international collaborator. Specifically, the project probes the fine structure of olfactory perception and tests the central hypothesis that feedback serves one or more of the following three mechanisms: predictive coding, attractor generation, or attention to enhance the discriminability of behaviorally relevant stimuli. The dynamics of: a) cortical-bulbar feedback, and b) olfactory bulb output neurons on which feedback acts indirectly via interneurons are monitored and subsequently modulated with cellular resolution in mice engaged in olfactory discrimination forced-choice tasks and contextual reversal learning tasks. Reversible optogenetic local suppression of cortical feedback in the olfactory bulb is combined with simultaneous two-photon resonant scanning imaging (100 Hz) of hundreds of neurons. To address the proposed feedback roles, specific experimental design is combined with machine learning tools and dynamical systems analysis.